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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
Introduction to the Sign Test01:10

Introduction to the Sign Test

The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

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Competitive Genomic Screens of Barcoded Yeast Libraries
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Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

A normalization strategy for comparing tag count data.

Koji Kadota1, Tomoaki Nishiyama, Kentaro Shimizu

  • 1Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@bi.a.u-tokyo.ac.jp.

Algorithms for Molecular Biology : AMB
|April 6, 2012
PubMed
Summary
This summary is machine-generated.

A new normalization strategy for RNA-sequencing (RNA-seq) data removes potential differentially expressed genes (DEGs) before normalization. This method improves accuracy for tag count data analysis, enhancing differential gene expression studies.

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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing (e.g., RNA-seq, ChIP-seq) generates tag count data for biological comparisons.
  • Accurate normalization is crucial for reliable differential gene expression analysis in RNA-seq.
  • Existing normalization methods require improvement for robust tag count data analysis.

Purpose of the Study:

  • To develop and evaluate a novel normalization strategy for tag count data, specifically for RNA-seq.
  • To enhance the accuracy of differential gene expression analysis by improving the normalization step.
  • To assess the performance of the proposed strategy against existing methods.

Main Methods:

  • A new normalization strategy was developed, involving the removal of potential differentially expressed genes (DEGs) prior to normalization factor calculation.
  • Eight combinations of R packages (edgeR, DESeq, baySeq, NBPSeq) were compared using their default settings versus the proposed normalization strategy.
  • Performance was evaluated using synthetic datasets across various scenarios, with the area under the curve (AUC) serving as the metric for sensitivity and specificity.

Main Results:

  • RNA-seq packages utilizing the proposed normalization strategy demonstrated superior performance across evaluated scenarios.
  • The effectiveness of the normalization strategy was validated using a real experimental dataset.
  • The strategy showed improved sensitivity and specificity in identifying true biological variations.

Conclusions:

  • Eliminating potential differentially expressed genes (DEGs) is a critical step for accurate RNA-seq data normalization.
  • The proposed normalization strategy offers improved accuracy for tag count data analysis.
  • This normalization concept is applicable to other tag count data types and microarray data analysis.